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Genetics: Original Article

Family-based Gene-by-environment Interaction Studies

Revelations and Remedies

Shi, Min; Umbach, David M.; Weinberg, Clarice R.

Author Information
doi: 10.1097/EDE.0b013e318212fec6


The etiology of most diseases is not purely genetic but involves both genetic variants and exposures. Consequently, along with genetic effects, investigators need to be able to assess environmental effects and gene-by-environment (G×E) interactions. Case-control studies can estimate all these effects but are vulnerable to bias due to population structure, a form of unmeasured confounding due to shared ancestry. Subpopulations that preferentially marry within themselves may differ in their frequencies of a particular allelic variant and also in their baseline risk of disease (the risk in noncarriers of the variant). When such population structure exists, failure to stratify on the ethnic subpopulations produces spurious associations between genotype and risk.

Family-based genetic association studies obviate the population-structure problem for genotype effects. When affected individuals and their parents are genotyped, one can condition on the parental genotypes and base inference on the apparent departures from Mendelian transmission that occur when susceptibility alleles are preferentially passed from parents to offspring who later develop the condition of interest. Valid analysis is achieved by either conditioning on the parents' genotypes explicitly, as in the conditional logistic approach,1–3 or implicitly through stratification, as in the log-linear likelihood approach.4 Other similar approaches also effectively base inference on transmission.5–8

Family-based approaches are also used to test G×E interactions. With case-parents data and a dichotomous exposure, an analysis for G×E can be carried out by comparing the estimated relative risks for exposed versus unexposed offspring.9 Other approaches are available for examining G×E with case-parents data for dichotomous as well as continuous exposures.3,10–13 All these approaches rely on an assumption that genotype and exposure are independent within families; that is, that, conditional on the parental genotypes, the inherited genotype does not influence propensity for exposure.

One problem implicit in case-parents data is the inability to estimate exposure effects, hindering the interpretability of any apparent G×E effects. To estimate exposure effects, one needs information about the exposure distribution in the population under study—information that is simply not available with a case-parents design. For a rare disease, the controls in a case-control study provide that information; in a disease-discordant sib-pair (case-sib) design, the unaffected sibling provides it.

The study design of the ongoing Two Sister Study is an alternative design that also provides exposure information. The Two Sister Study is a family-based study of genetic and environmental factors involved in the etiology of young-onset breast cancer. Women with breast cancer diagnosed under age 50 are enrolled along with a cancer-free sister. DNA is collected from the affected sister and their parents. Environmental exposures are ascertained from each sister by means of both environmental samples and extensive computer-assisted telephone interviews. This design augments case-parents data with exposure data from an unaffected sibling; we call it a tetrad design because 4 family members are studied. By staying within a family-based framework, inference about genetic effects remains robust to population structure. Exposure information from the sibling enables study of exposure effects. We expected analysis of this tetrad design to arise from a straightforward melding of the analyses of a case-parents design with a sibling-pair-matched case-control design, because likelihood inference for each design is possible through conditional logistic regression methods. Unfortunately, we learned that even nuclear–family-based analyses of G×E remain vulnerable to bias due to exposure-related population structure.

What has not been previously appreciated is that, given subpopulations distinct enough to produce preferential mating, many exposures might also vary across subpopulations. Suppose there is exposure-related population structure in the sense that subpopulations differ not only in their allele frequencies but also in their exposure distributions. In such a structured population, one might expect the correlation between a measured marker and any causative locus to differ across subpopulations. When subpopulation-specific exposure prevalence is correlated with the subpopulation-specific linkage disequilibrium (LD) between a marker and a causative locus, assessment of G×E interaction is subject to bias, even in family data. Simply stated, the exposure can act as a surrogate for the LD structure between the marker under study and a causative genetic variant such that the exposure and transmission at the marker may be correlated even when there is no interaction between the exposure and a causative genetic variant. Under such scenarios, typical family-based designs do not automatically protect against bias in assessing G×E interactions unless the locus under study is itself the causative locus and is not in LD with any other locus that is causally related to risk. Moreover, such favorable scenarios will be rare in practice, where SNPs being genotyped are typically markers that are associated with risk indirectly, through LD with nearby causative loci.

We use haplotype-based simulations to demonstrate that these biases occur and can dramatically increase the Type I error rate for existing family-based tests of multiplicative G×E interaction. To compare Type I error rates with the nominal, we consider analyses based on case-parents data, case-sib data, and data from our proposed tetrad design—all derived from a highly structured population with a dichotomous exposure. Although these initial results are discouraging, we go on to show that, when one has ascertained the exposure level for an unaffected sibling, a modification to the analysis of case-sib or tetrad data allows one to achieve the nominal Type I error rate despite exposure-related population structure.


Type I Error Rates of Existing Nuclear–family-based Methods

We studied the Type I error rates for tests of G×E involving a single SNP and a dichotomous exposure using 3 nuclear–family-based designs (case-parents, case-sib, and tetrad) and multiple approaches to their analysis. All the approaches are valid for testing G×E if there is no genetic population structure.

For case-parents data, one enrolls affected offspring and their biologic parents, genotyping all 3 individuals in each family and measuring an exposure for the offspring (Table). We studied pseudo-sibling analysis using conditional logistic regression.1,3 The conditional likelihood is that of a 1:3 matched case-control study where the case is matched to 3 pseudosibling controls, namely, the 3 possible offspring genotypes (other than that of the case) that could have been produced by the parents. The pseudosiblings all carry the same exposure as the case (the only exposure measured). For this design, we also studied a polytomous logistic regression approach, quantitative polytomous logistic regression,13 and a nonparametric approach, family-based association test - interaction.11

Information Collected for Different Designs

For case-sib data, one affected and one unaffected sibling are enrolled; both are genotyped and provide exposure information (Table). We studied the usual analysis based on the conditional likelihood for a 1:1 matched case-control study and an alternative analysis proposed by Chatterjee et al,14 which enforces assumed within-family gene-by-exposure independence by using the conditional likelihood for a 1:3 matched case-control study. In addition to the case and the control siblings, this likelihood uses 2 pseudo-sibling controls: one with the case's genotype and the control's exposure and another with the control's genotype and the case's exposure.

For tetrad data, one has case-parents data plus the recorded exposure for an unaffected sibling (Table). Our proposed analysis uses the conditional likelihood for a 1:7 matched case-control study. Given the parents' genotypes, 4 offspring genotypes are possible (that of the case and 3 pseudosiblings; these genotypes are not necessarily distinct). The 7 matched pseudosibling controls consist of these 4 genotypes, each with the unaffected sibling's exposure, and the 3 noncase genotypes, each with the case's exposure. This 1:7 matching enforces the within-family gene-by-exposure independence.14 To include the pedigree-based association test15 in our comparisons, we augmented the tetrad data with the unaffected sibling's genotype because this test requires the unaffected sibling's genotype to test for G×E for a dichotomous exposure.

To avoid bias from model misspecification in assessing G×E interaction, we saturated the model for both genetic and exposure main effects. To reduce the number of parameters, we used a single degree-of-freedom parameterization for interaction. Consequently, for a typical analysis of case-sib (with or without enforcing within-family independence) or an analysis of tetrad data with a dichotomous exposure, we fit a logistic regression model of the form:

For case-parents data, the term βEE was omitted as βE is not estimable. The nuisance parameters, αj, are neither estimable nor of interest. Here D is the disease indicator; Gi is an indicator that the child carries exactly i copies of the variant; GLIN = G1 + 2G2 is the number of copies of the variant allele that the child carries, and E is the indicator of the child's exposure. For a rare disease, the β parameters represent log-relative risks for the exposure and genetic main effects. The G×E interaction parameter, γLIN, models interaction through a single degree-of-freedom log-additive “trend” term. In the absence of population structure, validity for testing interaction is ensured despite possible misspecification of interaction terms if the above model is correct under the null.

Proposed Approach to Achieve Robustness

Suppose that when exposure participates in population structure, the covariance of genotype and offspring exposure (conditioning on parental genotypes) appears as a G×E interaction effect on risk in a naive analysis. This covariance might be separable into 2 components, one that represents the actual (within-family) G×E interaction effect and one that represents the spurious association (across families) attributable to the correlation of exposure with subpopulation identity (reflecting subpopulation-specific LD between marker and causative locus.) For designs where exposure is available for an unaffected sibling as well as the case, we propose the following logistic regression model. This model accounts for the spurious association and also protects against inflated Type I error rates induced when the exposure participates in population structure (see Appendix):

Here, I(Ē=0.5), for example, is an indicator that the average exposure for the case and control is 0.5 (exposed being 1 and unexposed being 0) and δ1 to δ4 are parameters that adjust the model for distortions of the genotype main effects due to an across-families association of exposure with subpopulation; other symbols are as defined earlier.


We simulated haplotypes based on HapMap-phased16 genotype data from the sample with European ancestry, using haplotypes and their frequencies for a 100-kb region around the replication factor C1 gene (RFC1). We constructed a haplotype set using 5 LD tagging SNPs for RFC1. These SNPs defined 12 haplotypes. We introduced a new locus as a causative SNP, residing on haplotype 1, so we considered 6 SNPs altogether. We simulated a dichotomous exposure assuming a rare-disease model. For each simulated scenario, we generated 1000 datasets, each with 1000 families. We generated families by sampling parental pairs (in scenarios with population stratification, both parents came from the same subpopulation), and then randomly generating 2 offspring based on Mendelian inheritance. Offspring exposures were randomly assigned according to the exposure prevalence for the corresponding subpopulation. We then generated disease status of a random one of the offspring based on his or her diplotypes and exposure, through the scenario's presumed risk model. Families with an affected offspring were retained until 1000 families were accrued. Imposing the rare disease assumption, the other sibling was taken to be unaffected. In the analysis, we fit the above models to each SNP separately and report G×E test results for individual SNPs. We also computed a multi-SNP test of G×E interaction by combining the single correlated SNP tests using Simes' procedure.17

To examine validity of the tests under population stratification, we simulated a no-interaction null scenario with a dichotomous exposure and 2 equal-sized subpopulations; each subpopulation had all haplotypes in Hardy-Weinberg equilibrium and the same baseline risk of disease. Risk-haplotype frequency and exposure prevalence were 0.1, 0.05, respectively, in one subpopulation and 0.9, 0.5, respectively, in the other. The haplotypes that were not associated with the risk allele occurred in the same relative frequencies as in HapMap. We set (R1, R2, I1, I2, Re) = (1, 3, 1, 1, 2) in each subpopulation, where Ri is the relative risk among the unexposed associated with inheritance of i copies of the causative allele, Re is the relative risk associated with having the exposure, and Ii is the interaction parameter defined as the ratio of the i-copy within-family relative risk among the exposed to that among the unexposed.

To study the influence of risk-allele frequency on power, we generated families either from a homogeneous population or from a stratified population formed from 2 equal-sized subpopulations, each in Hardy-Weinberg equilibrium. For the homogeneous scenarios, the exposure prevalence was set at 0.3 and the risk-haplotype frequency ranged from 0.1 to 0.5. For population-structured scenarios, the exposures in the 2 subpopulations were 0.05 and 0.4, respectively, and risk haplotype frequencies ranged from 0.1 to 0.5 in population 1 and, correspondingly, from 0.9 to 0.5 in population 2 (The scenario where both are 0.5 corresponds to an unstructured population). In these simulations, we set (R1, R2, I1, I2, Re) at (1, 3, 1.5, 2.25, 2). Thus, the risk model had interaction parameters where I2 = I12, which corresponded to the interaction parameterization that we used in fitting the simulated data. In general, of course, one need not know the proper interaction model. We do not show power for the pedigree-based association test because, as currently implemented, it requires log-additive genetic main effects for validity and so is invalid under our simulation scenario.


Simulations under a no-interaction null for a highly structured population where exposure participated in the structure revealed extreme inflation of the Type I error rate for all family-based designs and existing single-SNP analytic methods (Fig. 1A). The log-linear9 results are not shown because they were the same as those based on the case-parent pseudo-sibling analysis. Type I error rates for polytomous logistic and family-based association test - interaction were also inflated, but less so than for the other tests. Only for SNP 1, the causal locus, was Type I error consistent with the nominal 0.05. The exception was the pedigree-based association test; it showed inflated Type I error even at the causal locus, a feature attributable to its failure to saturate the genetic main effects (at least in the implementation available to us). All other individual SNPs as well as the multi-SNP test based on Simes' procedure showed a strong tendency to reject too often under the no-interaction null.

Simulation results on Type I error rate for tests of G×E interaction in a population with strong exposure-related stratification: A, commonly used methods; B, methods using proposed G Ē-adjustment. The abscissa indexes single SNP tests for SNPs 1 to 6, followed by a Simes' test for the 6 SNPs together. Symbols: analysis of case-parents data via the pseudosibling approach (open triangle), via family-based association test - interaction (filled square), and via quantitative polytomous logistic regression (open square); analysis of case-sib data via conditional logistic regression (filled circle) and via Chatterjee's method (open circle); analysis of tetrad data (filled triangle); analysis of tetrad data with unaffected sib genotyped via the pedigree-based association test (×).

When we analyzed the same data (either the tetrads or the sibling case-control) using our proposed alternative regression model, which included 4 additional covariates that allow the siblings' set of exposures to influence the main effects of genotype, the Type I error rates were consistent with the nominal 0.05 level for each SNP individually as well as for the multi-SNP test (Figure 1B). With case-parents data, this kind of alternative analysis is precluded by lack of supplementary exposure data.

When exposure does not participate in population structure, the usual family-based analyses of G×E interaction are valid, and inclusion of the terms involving the family-based Ē is unnecessary. In this situation, for the uncorrected multi-SNP test, the tetrad design had the best power (but required the most genotyping, 3 per family); the case-sib analysis via Chatterjee's method had intermediate power; the usual case-sib analysis had the lowest power. The power of case-parents analysis was slightly higher than that of the usual case-sib analysis but still much lower than that of the other designs (Fig. 2A). When terms involving Ē were included in these models, the power of each approach fell, as expected (Fig. 2B). In this scenario, the magnitude of the power loss was about the same for the 2 case-sib methods and was larger for the tetrad design. After adjustment, the tetrad analysis and the case-sib analysis via Chatterjee's method exhibited similar power.

Power of tests of G×E interaction for a homogeneous population under the risk scenario (R1, R2, I1, I2, Re) = (1, 3, 1.5, 2.25, 2): A, for unadjusted models; B, for models with (dashed line) and without (solid line) G Ē-adjustment (dashed line with filled triangles represents both the tetrad design and Chatterjee's method for the case-sib design because after G Ē-adjustment their power was the same so the curves coincide). Symbols: analysis of case-parents data via the pseudosibling approach (open triangle) and via FBAT-I (filled square); analysis of case-sib data via conditional logistic regression (filled circle) and via Chatterjee's method (open circle); analysis of tetrad data (filled triangle).

When exposure does participate in population structure, the usual family-based analyses of G×E interaction have inflated Type I error rates, as demonstrated. Adjustment by terms involving Ē is necessary for tests to have proper size. As expected, tests of interaction that did not adjust for Ē showed higher apparent power for the multi-SNP test than those that did (data not shown)—but in this situation such tests are invalid and would reject too often even under the null. Among the valid tests in this situation, the tetrad analysis exhibited the highest power, and its adjusted power was similar to that for Chatterjee's method (Fig. 3).

Power of valid tests of G×E interaction (those from G Ē-adjusted models) under a scenario with exposure-related stratification. After G Ē-adjustment, the power of the tetrad design coincides with that of the Chatterjee's method; therefore the dashed line with filled triangles represents both methods. Symbols: analysis of case-sib data via conditional logistic regression (filled circle); analysis of tetrad data (filled triangle).

Additional results for simulations under a broader range of scenarios are available in the eAppendix ( and the authors' Web site.18 The general ranking persists in these additional simulation studies.


Testing G×E interactions with family-based data has a bit of a checkered history. For a dichotomous exposure, interest properly centers on whether the relative risk associated with carrying a variant genotype differs between exposed and unexposed individuals. While Mendelian inheritance guarantees that family-based, ie, transmission-based, inferences about genetic effects are protected against inflated Type I error rates due to genetic population structure, our simulations document that this protection does not extend to inference related to gene-by-environment interaction.

One early method19 treated transmission of the variant allele as the dichotomous event of interest, and used logistic regression to compare transmission rates to unexposed versus exposed affected offspring. A similar and seductively simple method creates a two-by-two table based on categorizing all the heterozygous parents, with transmission/nontransmission of the designated allele forming the columns and exposed/unexposed (offspring) forming the rows.10 One simply carries out a χ2 test for independence. Unfortunately, while still used, such approaches that directly compare allelic transmission rates are invalid.9,20 First, they do not account for the induced dependency, present even in a homogeneous population, between transmissions from the mother and the father to an affected offspring. Second, and more importantly, in stratified populations transmission rates can differ between exposed and unexposed offspring even when the relative risks for carrying a variant genotype do not.

Other existing methods for testing interaction with case-parents data also in effect compare transmissions to exposed versus unexposed affected offspring, but do not use allelic transmission rates directly. These methods avoid the problems of transmission dependency by treating the family rather than each allelic transmission as the unit of analysis.

Many were developed with candidate SNPs in mind, that is, under the strong assumption that the SNP under study is causative and not in LD with another causative SNP. These methods are valid under that unrealistically narrow assumption, as shown in our simulations. Our simulations further demonstrate that these approaches are invalid generally for structured populations when the structure is exposure-related. The knotty problem is that population structure tends to produce heterogeneity in marker transmission rates even when genotype relative risks for the causative allele do not differ between exposed and unexposed individuals within families.

To demonstrate the potential for inflated Type I error rates when testing G×E interactions, we used an extreme scenario in our simulations. For less extreme scenarios, the inflation will be less. Of course, an investigator will generally not know how extreme exposure-related population structure may be in a targeted population. Other approaches to alleviate such bias include stratification on reported ethnicity or on strata derived from a large genome-wide panel of SNPs.21 The ability of such methods to overcome bias depends heavily on how well the assigned strata can identify sufficiently homogenous subpopulations. If families can be assigned unambiguously to their truly relevant subpopulation, then stratification will correct the inflation of Type I error rates for G×E; however, in most settings this expectation would be unrealistic.

Tests of interaction depend heavily on how one specifies the null model. The choice between the multiplicative versus the additive interaction null models has been a long-debated subject.22 The multiplicative model is widely used mainly due to the mathematical convenience of logistic regression, whereas the additive model has been argued to be more biologically relevant. We focused on testing a multiplicative null in this paper, but the tetrad design and the case-sib design also allow testing of an additive model.

Although results shown here have been restricted to a dichotomous exposure and a dichotomous phenotype, the same sorts of biases occur in the more general context where the exposure is continuous or even where the phenotype is quantitative. Biases can also occur in haplotype-based analyses when haplotypes under study are in LD with a causative SNP. Our simulations indicate that the Type I error rates are inflated for several haplotype-based approaches such as GEI-TRIMM23 PCPH,24 Unphased,25 Pseudocontrol26 (see Shi et al18). Thus, great care must be taken with inferences about G×E interactions when using family data. The usual analyses suggesting causative multiplicative interactions between an exposure and a genotype may simply be showing the tendency for exposure to serve as a marker for the LD relationship between the measured marker (even if a haplotype) and an unmeasured causative variant. It is worth noting that bias can occur even without differential LD in the subpopulations. Consider a scenario where a causative locus A is typed, but there is also another causative locus B. Bias can occur whenever both the haplotype frequencies and exposure prevalence differ in the 2 subpopulations even if the LD between loci A and B remains the same across subpopulations.27

Our proposed remedy is to adjust for a family-based measure of the exposure distribution, Ē, multiplied by genotype. This remedy works extremely well for a dichotomous exposure. If the exposure is continuous, then correcting for exposure-related population structure bias in assessing gene-by-environment interaction becomes more complex, and this problem is the subject of ongoing research.

Of the interaction analyses that used the G Ē-adjustment, the tetrad analysis was virtually identical to the sib-pair analysis that imposed within-family G-by-E independence. The other case-sib analysis was consistently less powerful. The within-family independence assumption used here to good advantage is far less stringent than independence of genotype and exposure in the general population (the assumption required for case-only analyses) and should often be plausible. Although this supports use of the case-sib method for assessing G×E, genotyping parents provides more power for testing genetic main effects and it permits additional questions to be addressed, eg, whether there are prenatal maternally mediated effects on risk,28 and whether the risk associated with a variant allele depends on the parent of origin.29

Although G Ē-adjustment removes bias from the assessment of G×E interaction, it also costs power in situations where adjustment is not needed. One could perform a 4-df (degree-of-freedom) likelihood ratio test by comparing the base genotype/exposure models with and without G Ē-adjustment to investigate whether inference in a particular data set will likely be subject to bias from population structure. To reduce the number of degrees of freedom, one can fit both G and Ē as linear, resulting in a 1-df likelihood ratio test. Unfortunately, even this 1-df test is not very sensitive. One can nevertheless set a liberal α-level (eg, α = 0.2) and use the model without G Ē-adjustment to achieve more power when the 1-df likelihood ratio test is not rejected. Empirical Bayes approaches could also potentially help the investigator to negotiate a compromise between bias and efficiency.30

The nonrobustness problem highlighted here for family-based analyses of genotype-by-exposure interaction also will plague family-based analyses of genotype-by-genotype interactions aimed at elucidating epistatic effects. Even for loci that are unlinked (eg, on different chromosomes), analyses can generate spurious evidence for epistasis if there is genetic population structure. A robust analysis for genotype-by-genotype interactions could be accomplished through stratification on parental genotypes at both loci (ie, 36 mating type strata), but this strategy would require a large sample size.

Most researchers, whether involved in the development or application of G×E methods, have mistakenly presumed that family-based methods must be robust to population structure. Under that mistaken assumption, investigators have scanned the genome SNP by SNP looking for G×E. We have shown that when the exposure participates in the population structure, the usual analyses of markers do not guarantee robust tests for G×E interaction effects. Recognition of this potential source of bias is particularly important for SNP-by-SNP analyses of family-based genome-wide association studies. Our proposed method provides one strategy for ameliorating the problem, at least for dichotomous exposures.


We thank Susan G. Komen for the Cure (FAS 0703856) for the support of the Two Sister Study and Kou Chia-Ling and Dmitri Zaykin for their careful review and valuable comments.


Adjustment for Bias in Testing Within-family G×E Interaction for a Categorical Exposure and a Marker SNP

Our simulations demonstrated nonrobustness of the G×E analysis using any of a number of methods. This issue arises for case-sib analyses because even after conditioning on both the exposure set for the sibling pair, {Ea, Eu}, and the genotype set for the sibling pair, {Ga, Gu}, if the exposure participates in population stratification, the product EG may be predictive of disease even in the absence of within-family causal multiplicative interaction. Spurious interaction arises because E can act as a marker for the subpopulation (ie, ancestry), hence, for the LD structure between the SNP marker under study and the causative SNP(s).

EG is predictive, however, only because the conditioning set {Ea, Eu} is itself predictive of the LD structure, hence of the main effects of the marker genotype. Let E denote a dichotomous exposure, which is either absent (E = 0) or present (E = 1). Let C denote the number of copies of the variant allele carried by the offspring at the marker. A realistic model is:

Here β1({Ea, Eu}), for example, denotes a parameter whose value is a function of a set to represent that the relative risk associated with inheriting a single copy of the variant allele can be a function of the observed set of exposures. If the analysis is structured so that any possible dependence of the main effect of G on the set {Ea, Eu} is accounted for, then the interaction parameters ω1 and ω2 will be 0 unless there is a multiplicative G×E interaction within families. If E is dichotomous, the argument for each of the 2 βc({Ea, Eu}) functions has 3 possible values, so we can saturate the main effects of G in a way that allows for heterogeneity of LD, by allowing 3 distinct values for each β coefficient. Thus, an analysis that fully stratifies in this way, by including 4 additional adjustment parameters, provides a robust test for within-family G×E interaction. The adjustment has an obvious extension for a multilevel categorical E, but how to saturate the G effects when E is continuous remains problematic.


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